Multi-turn scheduling assistants must preserve constraint edits and distinguish solver evidence from language-model interpretations. We present CoSched-Cert, a dialogue protocol combining a typed scheduling language, solver-distinguishing clarification, cost-based conflict localization, verified repair synthesis, and lexical certificate auditing. Its guarantees are conditional on the encoded problem and supplied candidate interpretations; lexical support does not establish semantic entailment. SchedNL-Bench-Cert contains 1,000 synthetic Chinese/English utterances and 350 sessions. Gold-AST replay measures internal consistency against labels generated by the same engine, while author-written protocol simulations illustrate selected design choices. Across three live-parser seeds on 52 sessions, session training and registered sibling augmentation raise conditional micro target-constraint agreement from 18.0% to 83.7%. The final configuration has 100% coarse status agreement but only 75.0% conflict-member agreement, and includes a rule informed by a test-session failure. We therefore treat these results as descriptive, not as an unbiased generalization estimate. We define a stricter joint-turn criterion and report the bounds supported by the available aggregates. The contribution is a protocol for exposing inspectable solver evidence and the remaining gap between encoded feasibility and user-intent correctness.

Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering various questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to the potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations on the fly, thereby improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs’ performance in long-context question answering with citations (LQAC), revealing considerable room for improvement. To this end, we propose CoF (Coarse to Fine), a novel pipeline that utilizes off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations, and leverage this pipeline to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we train LongCite-8B and LongCite-9B using the constructed dataset, successfully enabling the generation of accurate responses and fine-grained citations in one pass. The evaluation results on LongBench-Cite show that our trained models achieve state-of-the-art citation quality, surpassing advanced proprietary models including GPT-4o. We also discover that SFT with citation information can further improve the correctness of model responses compared to standard long-context SFT.